SDN-Sim: Integrating System Level Simulator with Software Defined Network

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. With the introduction of diverse technology paradigms in next-generation cellular and vehicular networks, design and structural complexity are skyrocketing. The beyond- 5G use cases such as mobile broadband, 5G-V2X and UAV communications require support for ultra-low latency and high throughput and reliability with limited operational complexity and cost. These use cases are being explored in 3GPP Release 16 and 17. To facilitate end-to-end performance evaluation for these applications, we propose SDN-Sim - an integration of a System Level Simulator (SLS) with a Software Defined Network (SDN) infrastructure. While the SLS models the communication channel and evaluates system performance on the physical and data link layers, the SDN performs network and application tasks such as routing, load balancing, etc. The proposed architecture replicates the SLS-defined topology into an SDN emulator for offloading control operations. It uses link and node information calculated by the SLS to compute routes in SDN and feeds the results back to the SLS. Along with the architecture, data modeling and processing, replication and route calculation frameworks are proposed

A Cognitive Routing framework for Self-Organised Knowledge Defined Networks

This study investigates the applicability of machine learning methods to the routing protocols for achieving rapid convergence in self-organized knowledge-defined networks. The research explores the constituents of the Self-Organized Networking (SON) paradigm for 5G and beyond, aiming to design a routing protocol that complies with the SON requirements. Further, it also exploits a contemporary discipline called Knowledge-Defined Networking (KDN) to extend the routing capability by calculating the “Most Reliable” path than the shortest one. The research identifies the potential key areas and possible techniques to meet the objectives by surveying the state-of-the-art of the relevant fields, such as QoS aware routing, Hybrid SDN architectures, intelligent routing models, and service migration techniques. The design phase focuses primarily on the mathematical modelling of the routing problem and approaches the solution by optimizing at the structural level. The work contributes Stochastic Temporal Edge Normalization (STEN) technique which fuses link and node utilization for cost calculation; MRoute, a hybrid routing algorithm for SDN that leverages STEN to provide constant-time convergence; Most Reliable Route First (MRRF) that uses a Recurrent Neural Network (RNN) to approximate route-reliability as the metric of MRRF. Additionally, the research outcomes include a cross-platform SDN Integration framework (SDN-SIM) and a secure migration technique for containerized services in a Multi-access Edge Computing environment using Distributed Ledger Technology. The research work now eyes the development of 6G standards and its compliance with Industry-5.0 for enhancing the abilities of the present outcomes in the light of Deep Reinforcement Learning and Quantum Computing